Overview

Dataset statistics

Number of variables34
Number of observations7146
Missing cells716
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 MiB
Average record size in memory272.0 B

Variable types

Boolean2
Categorical15
Numeric17

Alerts

bed_type is highly imbalanced (95.7%)Imbalance
bedrooms_na is highly imbalanced (99.6%)Imbalance
bathrooms_na is highly imbalanced (97.1%)Imbalance
beds_na is highly imbalanced (98.9%)Imbalance
bathrooms has 716 (10.0%) missing valuesMissing
bedrooms has 804 (11.3%) zerosZeros
beds has 87 (1.2%) zerosZeros
number_of_reviews has 1377 (19.3%) zerosZeros

Reproduction

Analysis started2023-12-18 01:42:39.626513
Analysis finished2023-12-18 01:43:18.774808
Duration39.15 seconds
Software versionydata-profiling vv4.6.3
Download configurationconfig.json

Variables

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
False
4215 
True
2931 
ValueCountFrequency (%)
False 4215
59.0%
True 2931
41.0%
2023-12-17T22:43:18.891486image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size56.0 KiB
strict_14_with_grace_period
3086 
moderate
2518 
flexible
1424 
super_strict_30
 
66
strict
 
43

Length

Max length27
Median length8
Mean length16.266583
Min length6

Characters and Unicode

Total characters116241
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmoderate
2nd rowstrict_14_with_grace_period
3rd rowstrict_14_with_grace_period
4th rowstrict_14_with_grace_period
5th rowstrict_14_with_grace_period

Common Values

ValueCountFrequency (%)
strict_14_with_grace_period 3086
43.2%
moderate 2518
35.2%
flexible 1424
19.9%
super_strict_30 66
 
0.9%
strict 43
 
0.6%
super_strict_60 9
 
0.1%

Length

2023-12-17T22:43:19.027483image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-17T22:43:19.157484image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
strict_14_with_grace_period 3086
43.2%
moderate 2518
35.2%
flexible 1424
19.9%
super_strict_30 66
 
0.9%
strict 43
 
0.6%
super_strict_60 9
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e 14131
12.2%
_ 12494
10.7%
t 12012
10.3%
r 11969
10.3%
i 10800
 
9.3%
c 6290
 
5.4%
a 5604
 
4.8%
d 5604
 
4.8%
o 5604
 
4.8%
s 3279
 
2.8%
Other values (15) 28454
24.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 97425
83.8%
Connector Punctuation 12494
 
10.7%
Decimal Number 6322
 
5.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 14131
14.5%
t 12012
12.3%
r 11969
12.3%
i 10800
11.1%
c 6290
 
6.5%
a 5604
 
5.8%
d 5604
 
5.8%
o 5604
 
5.8%
s 3279
 
3.4%
p 3161
 
3.2%
Other values (9) 18971
19.5%
Decimal Number
ValueCountFrequency (%)
4 3086
48.8%
1 3086
48.8%
0 75
 
1.2%
3 66
 
1.0%
6 9
 
0.1%
Connector Punctuation
ValueCountFrequency (%)
_ 12494
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 97425
83.8%
Common 18816
 
16.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 14131
14.5%
t 12012
12.3%
r 11969
12.3%
i 10800
11.1%
c 6290
 
6.5%
a 5604
 
5.8%
d 5604
 
5.8%
o 5604
 
5.8%
s 3279
 
3.4%
p 3161
 
3.2%
Other values (9) 18971
19.5%
Common
ValueCountFrequency (%)
_ 12494
66.4%
4 3086
 
16.4%
1 3086
 
16.4%
0 75
 
0.4%
3 66
 
0.4%
6 9
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 116241
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 14131
12.2%
_ 12494
10.7%
t 12012
10.3%
r 11969
10.3%
i 10800
 
9.3%
c 6290
 
5.4%
a 5604
 
4.8%
d 5604
 
4.8%
o 5604
 
4.8%
s 3279
 
2.8%
Other values (15) 28454
24.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
False
4430 
True
2716 
ValueCountFrequency (%)
False 4430
62.0%
True 2716
38.0%
2023-12-17T22:43:19.316791image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

host_total_listings_count
Real number (ℝ)

Distinct66
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.604954
Minimum0
Maximum1199
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2023-12-17T22:43:19.459445image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q38
95-th percentile439
Maximum1199
Range1199
Interquartile range (IQR)7

Descriptive statistics

Standard deviation177.42865
Coefficient of variation (CV)3.3728506
Kurtosis18.872986
Mean52.604954
Median Absolute Deviation (MAD)1
Skewness4.3664706
Sum375915
Variance31480.927
MonotonicityNot monotonic
2023-12-17T22:43:19.638445image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2650
37.1%
2 1190
16.7%
3 578
 
8.1%
4 437
 
6.1%
5 229
 
3.2%
852 204
 
2.9%
6 165
 
2.3%
165 141
 
2.0%
8 98
 
1.4%
439 91
 
1.3%
Other values (56) 1363
19.1%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 2650
37.1%
2 1190
16.7%
3 578
 
8.1%
4 437
 
6.1%
5 229
 
3.2%
6 165
 
2.3%
7 78
 
1.1%
8 98
 
1.4%
9 40
 
0.6%
ValueCountFrequency (%)
1199 35
 
0.5%
852 204
2.9%
799 27
 
0.4%
483 15
 
0.2%
439 91
1.3%
394 1
 
< 0.1%
200 17
 
0.2%
194 1
 
< 0.1%
165 141
2.0%
162 7
 
0.1%
Distinct36
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size56.0 KiB
Mission
691 
Western Addition
599 
South of Market
599 
Downtown/Civic Center
538 
Castro/Upper Market
 
405
Other values (31)
4314 

Length

Max length21
Median length16
Mean length13.057515
Min length6

Characters and Unicode

Total characters93309
Distinct characters46
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowWestern Addition
2nd rowBernal Heights
3rd rowHaight Ashbury
4th rowHaight Ashbury
5th rowWestern Addition

Common Values

ValueCountFrequency (%)
Mission 691
 
9.7%
Western Addition 599
 
8.4%
South of Market 599
 
8.4%
Downtown/Civic Center 538
 
7.5%
Castro/Upper Market 405
 
5.7%
Bernal Heights 373
 
5.2%
Haight Ashbury 351
 
4.9%
Noe Valley 317
 
4.4%
Outer Sunset 269
 
3.8%
Nob Hill 215
 
3.0%
Other values (26) 2789
39.0%

Length

2023-12-17T22:43:19.820121image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
market 1004
 
7.4%
mission 841
 
6.2%
of 715
 
5.3%
western 599
 
4.4%
addition 599
 
4.4%
south 599
 
4.4%
hill 588
 
4.3%
heights 575
 
4.2%
outer 566
 
4.2%
center 538
 
4.0%
Other values (39) 6988
51.3%

Most occurring characters

ValueCountFrequency (%)
e 8253
 
8.8%
i 8117
 
8.7%
t 7127
 
7.6%
n 6799
 
7.3%
6466
 
6.9%
o 6228
 
6.7%
r 6029
 
6.5%
s 5214
 
5.6%
a 4582
 
4.9%
l 2694
 
2.9%
Other values (36) 31800
34.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 72060
77.2%
Uppercase Letter 13840
 
14.8%
Space Separator 6466
 
6.9%
Other Punctuation 943
 
1.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 8253
11.5%
i 8117
11.3%
t 7127
9.9%
n 6799
9.4%
o 6228
8.6%
r 6029
8.4%
s 5214
 
7.2%
a 4582
 
6.4%
l 2694
 
3.7%
h 2679
 
3.7%
Other values (14) 14338
19.9%
Uppercase Letter
ValueCountFrequency (%)
M 2031
14.7%
C 1651
11.9%
H 1514
10.9%
S 1042
 
7.5%
A 1002
 
7.2%
P 779
 
5.6%
W 715
 
5.2%
D 691
 
5.0%
N 681
 
4.9%
B 679
 
4.9%
Other values (10) 3055
22.1%
Space Separator
ValueCountFrequency (%)
6466
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 943
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 85900
92.1%
Common 7409
 
7.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 8253
 
9.6%
i 8117
 
9.4%
t 7127
 
8.3%
n 6799
 
7.9%
o 6228
 
7.3%
r 6029
 
7.0%
s 5214
 
6.1%
a 4582
 
5.3%
l 2694
 
3.1%
h 2679
 
3.1%
Other values (34) 28178
32.8%
Common
ValueCountFrequency (%)
6466
87.3%
/ 943
 
12.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 93309
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 8253
 
8.8%
i 8117
 
8.7%
t 7127
 
7.6%
n 6799
 
7.3%
6466
 
6.9%
o 6228
 
6.7%
r 6029
 
6.5%
s 5214
 
5.6%
a 4582
 
4.9%
l 2694
 
2.9%
Other values (36) 31800
34.1%

latitude
Real number (ℝ)

Distinct4682
Distinct (%)65.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3430808.7
Minimum37.72
Maximum3781031
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2023-12-17T22:43:20.015123image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum37.72
5-th percentile377629
Q13774272.2
median3776317.5
Q33778220
95-th percentile3779875.8
Maximum3781031
Range3780993.3
Interquartile range (IQR)3947.75

Descriptive statistics

Standard deviation1033566.2
Coefficient of variation (CV)0.30126023
Kurtosis5.0806176
Mean3430808.7
Median Absolute Deviation (MAD)1976.5
Skewness-2.6583374
Sum2.4516559 × 1010
Variance1.0682591 × 1012
MonotonicityNot monotonic
2023-12-17T22:43:20.191121image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3778827 6
 
0.1%
3775369 6
 
0.1%
3778716 6
 
0.1%
3778724 6
 
0.1%
3778866 6
 
0.1%
3778811 6
 
0.1%
3778846 6
 
0.1%
3777981 6
 
0.1%
3776055 5
 
0.1%
3780031 5
 
0.1%
Other values (4672) 7088
99.2%
ValueCountFrequency (%)
37.72 1
 
< 0.1%
37.73 1
 
< 0.1%
37.78 3
< 0.1%
37.79 1
 
< 0.1%
37716 1
 
< 0.1%
37717 1
 
< 0.1%
37719 1
 
< 0.1%
37728 1
 
< 0.1%
37729 1
 
< 0.1%
37731 1
 
< 0.1%
ValueCountFrequency (%)
3781031 1
 
< 0.1%
3780757 1
 
< 0.1%
3780738 1
 
< 0.1%
3780728 1
 
< 0.1%
3780725 1
 
< 0.1%
3780661 1
 
< 0.1%
3780658 3
< 0.1%
3780657 1
 
< 0.1%
3780646 1
 
< 0.1%
3780645 1
 
< 0.1%

longitude
Real number (ℝ)

Distinct4787
Distinct (%)67.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-11147194
Minimum-12251306
Maximum-122.39
Zeros0
Zeros (%)0.0%
Negative7146
Negative (%)100.0%
Memory size56.0 KiB
2023-12-17T22:43:20.364815image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-12251306
5-th percentile-12248724
Q1-12244084
median-12242270
Q3-12240768
95-th percentile-1224214
Maximum-122.39
Range12251184
Interquartile range (IQR)3316

Descriptive statistics

Standard deviation3319669.5
Coefficient of variation (CV)-0.29780316
Kurtosis5.3235852
Mean-11147194
Median Absolute Deviation (MAD)1621.5
Skewness2.7033304
Sum-7.9657845 × 1010
Variance1.1020205 × 1013
MonotonicityNot monotonic
2023-12-17T22:43:20.546813image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-12243386 6
 
0.1%
-12241938 6
 
0.1%
-12241153 6
 
0.1%
-12242263 6
 
0.1%
-12240798 6
 
0.1%
-12240678 6
 
0.1%
-12240958 6
 
0.1%
-12242977 6
 
0.1%
-12242399 6
 
0.1%
-12240874 5
 
0.1%
Other values (4777) 7087
99.2%
ValueCountFrequency (%)
-12251306 1
< 0.1%
-12251163 1
< 0.1%
-12251117 1
< 0.1%
-12251015 1
< 0.1%
-12250968 1
< 0.1%
-12250964 1
< 0.1%
-12250957 1
< 0.1%
-12250952 1
< 0.1%
-12250937 1
< 0.1%
-12250936 2
< 0.1%
ValueCountFrequency (%)
-122.39 1
 
< 0.1%
-122.41 2
< 0.1%
-122.43 4
0.1%
-122.44 1
 
< 0.1%
-122.45 1
 
< 0.1%
-122.47 1
 
< 0.1%
-122388 2
< 0.1%
-122392 1
 
< 0.1%
-122394 2
< 0.1%
-122395 2
< 0.1%

property_type
Categorical

Distinct26
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size56.0 KiB
Apartment
3010 
House
1990 
Condominium
760 
Guest suite
496 
Boutique hotel
 
183
Other values (21)
707 

Length

Max length18
Median length17
Mean length8.3664987
Min length4

Characters and Unicode

Total characters59787
Distinct characters38
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.1%

Sample

1st rowApartment
2nd rowApartment
3rd rowApartment
4th rowApartment
5th rowHouse

Common Values

ValueCountFrequency (%)
Apartment 3010
42.1%
House 1990
27.8%
Condominium 760
 
10.6%
Guest suite 496
 
6.9%
Boutique hotel 183
 
2.6%
Townhouse 140
 
2.0%
Serviced apartment 116
 
1.6%
Hotel 100
 
1.4%
Loft 93
 
1.3%
Hostel 87
 
1.2%
Other values (16) 171
 
2.4%

Length

2023-12-17T22:43:20.718814image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
apartment 3126
39.1%
house 1994
24.9%
condominium 760
 
9.5%
guest 496
 
6.2%
suite 496
 
6.2%
hotel 283
 
3.5%
boutique 183
 
2.3%
townhouse 140
 
1.7%
serviced 116
 
1.4%
loft 93
 
1.2%
Other values (19) 316
 
3.9%

Most occurring characters

ValueCountFrequency (%)
t 8053
13.5%
e 7247
12.1%
n 4839
 
8.1%
m 4647
 
7.8%
o 4540
 
7.6%
u 4358
 
7.3%
a 3393
 
5.7%
s 3341
 
5.6%
r 3324
 
5.6%
p 3146
 
5.3%
Other values (28) 12899
21.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 51783
86.6%
Uppercase Letter 7146
 
12.0%
Space Separator 857
 
1.4%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 8053
15.6%
e 7247
14.0%
n 4839
9.3%
m 4647
9.0%
o 4540
8.8%
u 4358
8.4%
a 3393
6.6%
s 3341
6.5%
r 3324
6.4%
p 3146
 
6.1%
Other values (13) 4895
9.5%
Uppercase Letter
ValueCountFrequency (%)
A 3030
42.4%
H 2177
30.5%
C 772
 
10.8%
G 540
 
7.6%
B 231
 
3.2%
T 145
 
2.0%
S 116
 
1.6%
L 93
 
1.3%
O 22
 
0.3%
V 10
 
0.1%
Other values (3) 10
 
0.1%
Space Separator
ValueCountFrequency (%)
857
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 58929
98.6%
Common 858
 
1.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 8053
13.7%
e 7247
12.3%
n 4839
8.2%
m 4647
 
7.9%
o 4540
 
7.7%
u 4358
 
7.4%
a 3393
 
5.8%
s 3341
 
5.7%
r 3324
 
5.6%
p 3146
 
5.3%
Other values (26) 12041
20.4%
Common
ValueCountFrequency (%)
857
99.9%
- 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 59787
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 8053
13.5%
e 7247
12.1%
n 4839
 
8.1%
m 4647
 
7.8%
o 4540
 
7.6%
u 4358
 
7.3%
a 3393
 
5.7%
s 3341
 
5.6%
r 3324
 
5.6%
p 3146
 
5.3%
Other values (28) 12899
21.6%

room_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.0 KiB
Entire home/apt
4364 
Private room
2603 
Shared room
 
179

Length

Max length15
Median length15
Mean length13.807025
Min length11

Characters and Unicode

Total characters98665
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEntire home/apt
2nd rowEntire home/apt
3rd rowPrivate room
4th rowPrivate room
5th rowEntire home/apt

Common Values

ValueCountFrequency (%)
Entire home/apt 4364
61.1%
Private room 2603
36.4%
Shared room 179
 
2.5%

Length

2023-12-17T22:43:20.871813image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-17T22:43:21.008814image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
entire 4364
30.5%
home/apt 4364
30.5%
room 2782
19.5%
private 2603
18.2%
shared 179
 
1.3%

Most occurring characters

ValueCountFrequency (%)
e 11510
11.7%
t 11331
11.5%
o 9928
10.1%
r 9928
10.1%
a 7146
 
7.2%
7146
 
7.2%
m 7146
 
7.2%
i 6967
 
7.1%
h 4543
 
4.6%
p 4364
 
4.4%
Other values (7) 18656
18.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 80009
81.1%
Space Separator 7146
 
7.2%
Uppercase Letter 7146
 
7.2%
Other Punctuation 4364
 
4.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 11510
14.4%
t 11331
14.2%
o 9928
12.4%
r 9928
12.4%
a 7146
8.9%
m 7146
8.9%
i 6967
8.7%
h 4543
 
5.7%
p 4364
 
5.5%
n 4364
 
5.5%
Other values (2) 2782
 
3.5%
Uppercase Letter
ValueCountFrequency (%)
E 4364
61.1%
P 2603
36.4%
S 179
 
2.5%
Space Separator
ValueCountFrequency (%)
7146
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 4364
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 87155
88.3%
Common 11510
 
11.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 11510
13.2%
t 11331
13.0%
o 9928
11.4%
r 9928
11.4%
a 7146
8.2%
m 7146
8.2%
i 6967
8.0%
h 4543
 
5.2%
p 4364
 
5.0%
E 4364
 
5.0%
Other values (5) 9928
11.4%
Common
ValueCountFrequency (%)
7146
62.1%
/ 4364
37.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 98665
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 11510
11.7%
t 11331
11.5%
o 9928
10.1%
r 9928
10.1%
a 7146
 
7.2%
7146
 
7.2%
m 7146
 
7.2%
i 6967
 
7.1%
h 4543
 
4.6%
p 4364
 
4.4%
Other values (7) 18656
18.9%

accommodates
Real number (ℝ)

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2010915
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2023-12-17T22:43:21.152945image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q34
95-th percentile7
Maximum16
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9149163
Coefficient of variation (CV)0.59820729
Kurtosis4.403458
Mean3.2010915
Median Absolute Deviation (MAD)1
Skewness1.7024419
Sum22875
Variance3.6669043
MonotonicityNot monotonic
2023-12-17T22:43:21.295946image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2 3208
44.9%
4 1461
20.4%
1 629
 
8.8%
3 582
 
8.1%
6 568
 
7.9%
5 322
 
4.5%
8 174
 
2.4%
7 95
 
1.3%
10 44
 
0.6%
9 22
 
0.3%
Other values (6) 41
 
0.6%
ValueCountFrequency (%)
1 629
 
8.8%
2 3208
44.9%
3 582
 
8.1%
4 1461
20.4%
5 322
 
4.5%
6 568
 
7.9%
7 95
 
1.3%
8 174
 
2.4%
9 22
 
0.3%
10 44
 
0.6%
ValueCountFrequency (%)
16 3
 
< 0.1%
15 7
 
0.1%
14 4
 
0.1%
13 1
 
< 0.1%
12 19
 
0.3%
11 7
 
0.1%
10 44
 
0.6%
9 22
 
0.3%
8 174
2.4%
7 95
1.3%

bathrooms
Real number (ℝ)

MISSING 

Distinct11
Distinct (%)0.2%
Missing716
Missing (%)10.0%
Infinite0
Infinite (%)0.0%
Mean1.2608865
Minimum0
Maximum14
Zeros39
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2023-12-17T22:43:21.428946image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum14
Range14
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.77572168
Coefficient of variation (CV)0.61521929
Kurtosis56.684037
Mean1.2608865
Median Absolute Deviation (MAD)0
Skewness6.0818971
Sum8107.5
Variance0.60174412
MonotonicityNot monotonic
2023-12-17T22:43:21.553946image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 5145
72.0%
2 1005
 
14.1%
3 132
 
1.8%
0 39
 
0.5%
4 32
 
0.4%
5 26
 
0.4%
0.5 17
 
0.2%
8 15
 
0.2%
10 12
 
0.2%
6 6
 
0.1%
(Missing) 716
 
10.0%
ValueCountFrequency (%)
0 39
 
0.5%
0.5 17
 
0.2%
1 5145
72.0%
2 1005
 
14.1%
3 132
 
1.8%
4 32
 
0.4%
5 26
 
0.4%
6 6
 
0.1%
8 15
 
0.2%
10 12
 
0.2%
ValueCountFrequency (%)
14 1
 
< 0.1%
10 12
 
0.2%
8 15
 
0.2%
6 6
 
0.1%
5 26
 
0.4%
4 32
 
0.4%
3 132
 
1.8%
2 1005
 
14.1%
1 5145
72.0%
0.5 17
 
0.2%

bedrooms
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3427092
Minimum0
Maximum14
Zeros804
Zeros (%)11.3%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2023-12-17T22:43:21.671946image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile3
Maximum14
Range14
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.93285457
Coefficient of variation (CV)0.69475548
Kurtosis6.7638207
Mean1.3427092
Median Absolute Deviation (MAD)0
Skewness1.537403
Sum9595
Variance0.87021766
MonotonicityNot monotonic
2023-12-17T22:43:21.791945image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 4199
58.8%
2 1304
 
18.2%
0 804
 
11.3%
3 627
 
8.8%
4 175
 
2.4%
5 25
 
0.3%
6 9
 
0.1%
7 2
 
< 0.1%
14 1
 
< 0.1%
ValueCountFrequency (%)
0 804
 
11.3%
1 4199
58.8%
2 1304
 
18.2%
3 627
 
8.8%
4 175
 
2.4%
5 25
 
0.3%
6 9
 
0.1%
7 2
 
< 0.1%
14 1
 
< 0.1%
ValueCountFrequency (%)
14 1
 
< 0.1%
7 2
 
< 0.1%
6 9
 
0.1%
5 25
 
0.3%
4 175
 
2.4%
3 627
 
8.8%
2 1304
 
18.2%
1 4199
58.8%
0 804
 
11.3%

beds
Real number (ℝ)

ZEROS 

Distinct14
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7644836
Minimum0
Maximum14
Zeros87
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2023-12-17T22:43:21.909945image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum14
Range14
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1767889
Coefficient of variation (CV)0.66693106
Kurtosis7.9429404
Mean1.7644836
Median Absolute Deviation (MAD)0
Skewness2.1819607
Sum12609
Variance1.3848322
MonotonicityNot monotonic
2023-12-17T22:43:22.032946image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1 3896
54.5%
2 1810
25.3%
3 727
 
10.2%
4 409
 
5.7%
5 122
 
1.7%
0 87
 
1.2%
6 46
 
0.6%
7 27
 
0.4%
8 12
 
0.2%
10 5
 
0.1%
Other values (4) 5
 
0.1%
ValueCountFrequency (%)
0 87
 
1.2%
1 3896
54.5%
2 1810
25.3%
3 727
 
10.2%
4 409
 
5.7%
5 122
 
1.7%
6 46
 
0.6%
7 27
 
0.4%
8 12
 
0.2%
9 2
 
< 0.1%
ValueCountFrequency (%)
14 1
 
< 0.1%
12 1
 
< 0.1%
11 1
 
< 0.1%
10 5
 
0.1%
9 2
 
< 0.1%
8 12
 
0.2%
7 27
 
0.4%
6 46
 
0.6%
5 122
 
1.7%
4 409
5.7%

bed_type
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size56.0 KiB
Real Bed
7073 
Futon
 
32
Pull-out Sofa
 
23
Airbed
 
11
Couch
 
7

Length

Max length13
Median length8
Mean length7.9966415
Min length5

Characters and Unicode

Total characters57144
Distinct characters23
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowReal Bed
2nd rowReal Bed
3rd rowReal Bed
4th rowReal Bed
5th rowReal Bed

Common Values

ValueCountFrequency (%)
Real Bed 7073
99.0%
Futon 32
 
0.4%
Pull-out Sofa 23
 
0.3%
Airbed 11
 
0.2%
Couch 7
 
0.1%

Length

2023-12-17T22:43:22.195945image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-17T22:43:22.344949image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
real 7073
49.7%
bed 7073
49.7%
futon 32
 
0.2%
pull-out 23
 
0.2%
sofa 23
 
0.2%
airbed 11
 
0.1%
couch 7
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 14157
24.8%
l 7119
12.5%
a 7096
12.4%
7096
12.4%
d 7084
12.4%
R 7073
12.4%
B 7073
12.4%
o 85
 
0.1%
u 85
 
0.1%
t 55
 
0.1%
Other values (13) 221
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 35783
62.6%
Uppercase Letter 14242
 
24.9%
Space Separator 7096
 
12.4%
Dash Punctuation 23
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 14157
39.6%
l 7119
19.9%
a 7096
19.8%
d 7084
19.8%
o 85
 
0.2%
u 85
 
0.2%
t 55
 
0.2%
n 32
 
0.1%
f 23
 
0.1%
i 11
 
< 0.1%
Other values (4) 36
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
R 7073
49.7%
B 7073
49.7%
F 32
 
0.2%
P 23
 
0.2%
S 23
 
0.2%
A 11
 
0.1%
C 7
 
< 0.1%
Space Separator
ValueCountFrequency (%)
7096
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 50025
87.5%
Common 7119
 
12.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 14157
28.3%
l 7119
14.2%
a 7096
14.2%
d 7084
14.2%
R 7073
14.1%
B 7073
14.1%
o 85
 
0.2%
u 85
 
0.2%
t 55
 
0.1%
n 32
 
0.1%
Other values (11) 166
 
0.3%
Common
ValueCountFrequency (%)
7096
99.7%
- 23
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 57144
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 14157
24.8%
l 7119
12.5%
a 7096
12.4%
7096
12.4%
d 7084
12.4%
R 7073
12.4%
B 7073
12.4%
o 85
 
0.1%
u 85
 
0.1%
t 55
 
0.1%
Other values (13) 221
 
0.4%

minimum_nights
Real number (ℝ)

Distinct45
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.8178
Minimum1
Maximum365
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2023-12-17T22:43:22.527945image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q330
95-th percentile30
Maximum365
Range364
Interquartile range (IQR)28

Descriptive statistics

Standard deviation22.511624
Coefficient of variation (CV)1.423183
Kurtosis78.320567
Mean15.8178
Median Absolute Deviation (MAD)3
Skewness6.4640853
Sum113034
Variance506.77324
MonotonicityNot monotonic
2023-12-17T22:43:22.686945image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
30 2757
38.6%
2 1455
20.4%
1 1251
17.5%
3 822
 
11.5%
4 270
 
3.8%
5 176
 
2.5%
31 133
 
1.9%
7 72
 
1.0%
60 32
 
0.4%
6 31
 
0.4%
Other values (35) 147
 
2.1%
ValueCountFrequency (%)
1 1251
17.5%
2 1455
20.4%
3 822
11.5%
4 270
 
3.8%
5 176
 
2.5%
6 31
 
0.4%
7 72
 
1.0%
8 1
 
< 0.1%
9 1
 
< 0.1%
10 3
 
< 0.1%
ValueCountFrequency (%)
365 7
 
0.1%
360 1
 
< 0.1%
270 1
 
< 0.1%
200 1
 
< 0.1%
188 1
 
< 0.1%
183 2
 
< 0.1%
180 28
0.4%
170 1
 
< 0.1%
140 1
 
< 0.1%
120 6
 
0.1%

number_of_reviews
Real number (ℝ)

ZEROS 

Distinct372
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.553876
Minimum0
Maximum677
Zeros1377
Zeros (%)19.3%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2023-12-17T22:43:22.845945image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median11
Q354
95-th percentile195
Maximum677
Range677
Interquartile range (IQR)53

Descriptive statistics

Standard deviation72.538481
Coefficient of variation (CV)1.6654885
Kurtosis10.420218
Mean43.553876
Median Absolute Deviation (MAD)11
Skewness2.8326394
Sum311236
Variance5261.8312
MonotonicityNot monotonic
2023-12-17T22:43:23.009945image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1377
 
19.3%
1 527
 
7.4%
2 353
 
4.9%
3 235
 
3.3%
4 188
 
2.6%
5 176
 
2.5%
6 172
 
2.4%
7 144
 
2.0%
8 103
 
1.4%
9 103
 
1.4%
Other values (362) 3768
52.7%
ValueCountFrequency (%)
0 1377
19.3%
1 527
 
7.4%
2 353
 
4.9%
3 235
 
3.3%
4 188
 
2.6%
5 176
 
2.5%
6 172
 
2.4%
7 144
 
2.0%
8 103
 
1.4%
9 103
 
1.4%
ValueCountFrequency (%)
677 1
< 0.1%
647 1
< 0.1%
608 1
< 0.1%
602 1
< 0.1%
576 1
< 0.1%
559 1
< 0.1%
540 1
< 0.1%
520 1
< 0.1%
517 1
< 0.1%
515 1
< 0.1%

review_scores_rating
Real number (ℝ)

Distinct39
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.034285
Minimum20
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2023-12-17T22:43:23.328946image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile86
Q195
median98
Q399
95-th percentile100
Maximum100
Range80
Interquartile range (IQR)4

Descriptive statistics

Standard deviation6.2861395
Coefficient of variation (CV)0.065457242
Kurtosis38.678256
Mean96.034285
Median Absolute Deviation (MAD)2
Skewness-4.9436347
Sum686261
Variance39.515549
MonotonicityNot monotonic
2023-12-17T22:43:23.480948image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
98 2098
29.4%
100 1603
22.4%
99 617
 
8.6%
97 596
 
8.3%
96 418
 
5.8%
95 348
 
4.9%
93 249
 
3.5%
94 234
 
3.3%
90 167
 
2.3%
92 141
 
2.0%
Other values (29) 675
 
9.4%
ValueCountFrequency (%)
20 8
 
0.1%
30 1
 
< 0.1%
40 6
 
0.1%
50 2
 
< 0.1%
56 1
 
< 0.1%
60 44
0.6%
63 1
 
< 0.1%
64 1
 
< 0.1%
67 4
 
0.1%
70 21
0.3%
ValueCountFrequency (%)
100 1603
22.4%
99 617
 
8.6%
98 2098
29.4%
97 596
 
8.3%
96 418
 
5.8%
95 348
 
4.9%
94 234
 
3.3%
93 249
 
3.5%
92 141
 
2.0%
91 108
 
1.5%

review_scores_accuracy
Real number (ℝ)

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.8203191
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2023-12-17T22:43:23.610945image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile9
Q110
median10
Q310
95-th percentile10
Maximum10
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6019899
Coefficient of variation (CV)0.061300442
Kurtosis54.168457
Mean9.8203191
Median Absolute Deviation (MAD)0
Skewness-6.0853935
Sum70176
Variance0.36239184
MonotonicityNot monotonic
2023-12-17T22:43:23.736946image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
10 6227
87.1%
9 733
 
10.3%
8 113
 
1.6%
6 25
 
0.3%
7 25
 
0.3%
4 9
 
0.1%
2 8
 
0.1%
5 5
 
0.1%
3 1
 
< 0.1%
ValueCountFrequency (%)
2 8
 
0.1%
3 1
 
< 0.1%
4 9
 
0.1%
5 5
 
0.1%
6 25
 
0.3%
7 25
 
0.3%
8 113
 
1.6%
9 733
 
10.3%
10 6227
87.1%
ValueCountFrequency (%)
10 6227
87.1%
9 733
 
10.3%
8 113
 
1.6%
7 25
 
0.3%
6 25
 
0.3%
5 5
 
0.1%
4 9
 
0.1%
3 1
 
< 0.1%
2 8
 
0.1%

review_scores_cleanliness
Real number (ℝ)

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.6995522
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2023-12-17T22:43:23.857946image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile8
Q110
median10
Q310
95-th percentile10
Maximum10
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.70387695
Coefficient of variation (CV)0.072567985
Kurtosis22.246043
Mean9.6995522
Median Absolute Deviation (MAD)0
Skewness-3.7689897
Sum69313
Variance0.49544277
MonotonicityNot monotonic
2023-12-17T22:43:23.985680image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
10 5608
78.5%
9 1149
 
16.1%
8 271
 
3.8%
7 61
 
0.9%
6 39
 
0.5%
4 10
 
0.1%
2 5
 
0.1%
5 2
 
< 0.1%
3 1
 
< 0.1%
ValueCountFrequency (%)
2 5
 
0.1%
3 1
 
< 0.1%
4 10
 
0.1%
5 2
 
< 0.1%
6 39
 
0.5%
7 61
 
0.9%
8 271
 
3.8%
9 1149
 
16.1%
10 5608
78.5%
ValueCountFrequency (%)
10 5608
78.5%
9 1149
 
16.1%
8 271
 
3.8%
7 61
 
0.9%
6 39
 
0.5%
5 2
 
< 0.1%
4 10
 
0.1%
3 1
 
< 0.1%
2 5
 
0.1%

review_scores_checkin
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.8958858
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2023-12-17T22:43:24.107681image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile9
Q110
median10
Q310
95-th percentile10
Maximum10
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.44870376
Coefficient of variation (CV)0.045342455
Kurtosis92.460478
Mean9.8958858
Median Absolute Deviation (MAD)0
Skewness-7.6966366
Sum70716
Variance0.20133507
MonotonicityNot monotonic
2023-12-17T22:43:24.233684image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
10 6603
92.4%
9 426
 
6.0%
8 78
 
1.1%
7 18
 
0.3%
6 14
 
0.2%
2 5
 
0.1%
4 2
 
< 0.1%
ValueCountFrequency (%)
2 5
 
0.1%
4 2
 
< 0.1%
6 14
 
0.2%
7 18
 
0.3%
8 78
 
1.1%
9 426
 
6.0%
10 6603
92.4%
ValueCountFrequency (%)
10 6603
92.4%
9 426
 
6.0%
8 78
 
1.1%
7 18
 
0.3%
6 14
 
0.2%
4 2
 
< 0.1%
2 5
 
0.1%
Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.872796
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2023-12-17T22:43:24.356682image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile9
Q110
median10
Q310
95-th percentile10
Maximum10
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.52259935
Coefficient of variation (CV)0.052933268
Kurtosis83.453928
Mean9.872796
Median Absolute Deviation (MAD)0
Skewness-7.5857028
Sum70551
Variance0.27311008
MonotonicityNot monotonic
2023-12-17T22:43:24.483360image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
10 6502
91.0%
9 511
 
7.2%
8 81
 
1.1%
7 19
 
0.3%
6 17
 
0.2%
2 8
 
0.1%
4 7
 
0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
2 8
 
0.1%
4 7
 
0.1%
5 1
 
< 0.1%
6 17
 
0.2%
7 19
 
0.3%
8 81
 
1.1%
9 511
 
7.2%
10 6502
91.0%
ValueCountFrequency (%)
10 6502
91.0%
9 511
 
7.2%
8 81
 
1.1%
7 19
 
0.3%
6 17
 
0.2%
5 1
 
< 0.1%
4 7
 
0.1%
2 8
 
0.1%

review_scores_location
Real number (ℝ)

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.7191436
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2023-12-17T22:43:24.611081image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile9
Q110
median10
Q310
95-th percentile10
Maximum10
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.65925305
Coefficient of variation (CV)0.067830364
Kurtosis30.704465
Mean9.7191436
Median Absolute Deviation (MAD)0
Skewness-4.2950041
Sum69453
Variance0.43461458
MonotonicityNot monotonic
2023-12-17T22:43:24.735083image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
10 5584
78.1%
9 1302
 
18.2%
8 176
 
2.5%
7 33
 
0.5%
6 32
 
0.4%
4 11
 
0.2%
2 6
 
0.1%
3 1
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
2 6
 
0.1%
3 1
 
< 0.1%
4 11
 
0.2%
5 1
 
< 0.1%
6 32
 
0.4%
7 33
 
0.5%
8 176
 
2.5%
9 1302
 
18.2%
10 5584
78.1%
ValueCountFrequency (%)
10 5584
78.1%
9 1302
 
18.2%
8 176
 
2.5%
7 33
 
0.5%
6 32
 
0.4%
5 1
 
< 0.1%
4 11
 
0.2%
3 1
 
< 0.1%
2 6
 
0.1%

review_scores_value
Real number (ℝ)

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.5243493
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2023-12-17T22:43:24.855764image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile8
Q19
median10
Q310
95-th percentile10
Maximum10
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.75160321
Coefficient of variation (CV)0.078913864
Kurtosis17.566905
Mean9.5243493
Median Absolute Deviation (MAD)0
Skewness-2.976255
Sum68061
Variance0.56490738
MonotonicityNot monotonic
2023-12-17T22:43:24.981767image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
10 4409
61.7%
9 2307
32.3%
8 319
 
4.5%
7 45
 
0.6%
6 44
 
0.6%
4 9
 
0.1%
2 8
 
0.1%
5 5
 
0.1%
ValueCountFrequency (%)
2 8
 
0.1%
4 9
 
0.1%
5 5
 
0.1%
6 44
 
0.6%
7 45
 
0.6%
8 319
 
4.5%
9 2307
32.3%
10 4409
61.7%
ValueCountFrequency (%)
10 4409
61.7%
9 2307
32.3%
8 319
 
4.5%
7 45
 
0.6%
6 44
 
0.6%
5 5
 
0.1%
4 9
 
0.1%
2 8
 
0.1%

price
Real number (ℝ)

Distinct475
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean213.30982
Minimum10
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2023-12-17T22:43:25.135764image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile55
Q1100
median150
Q3235
95-th percentile529
Maximum10000
Range9990
Interquartile range (IQR)135

Descriptive statistics

Standard deviation311.3755
Coefficient of variation (CV)1.4597335
Kurtosis407.56155
Mean213.30982
Median Absolute Deviation (MAD)60
Skewness16.323317
Sum1524312
Variance96954.701
MonotonicityNot monotonic
2023-12-17T22:43:25.298765image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150 287
 
4.0%
100 197
 
2.8%
200 194
 
2.7%
250 178
 
2.5%
125 161
 
2.3%
300 124
 
1.7%
120 123
 
1.7%
140 122
 
1.7%
99 117
 
1.6%
110 110
 
1.5%
Other values (465) 5533
77.4%
ValueCountFrequency (%)
10 2
 
< 0.1%
19 3
 
< 0.1%
27 1
 
< 0.1%
28 2
 
< 0.1%
29 2
 
< 0.1%
30 15
0.2%
31 1
 
< 0.1%
32 3
 
< 0.1%
33 11
0.2%
34 1
 
< 0.1%
ValueCountFrequency (%)
10000 1
 
< 0.1%
9000 1
 
< 0.1%
8000 3
< 0.1%
4500 2
< 0.1%
3800 1
 
< 0.1%
3394 1
 
< 0.1%
3250 1
 
< 0.1%
3050 1
 
< 0.1%
3000 1
 
< 0.1%
2450 1
 
< 0.1%

bedrooms_na
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.0 KiB
0.0
7144 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters21438
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7144
> 99.9%
1.0 2
 
< 0.1%

Length

2023-12-17T22:43:25.447766image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-17T22:43:25.557424image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7144
> 99.9%
1.0 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 14290
66.7%
. 7146
33.3%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14292
66.7%
Other Punctuation 7146
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14290
> 99.9%
1 2
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 7146
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21438
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14290
66.7%
. 7146
33.3%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21438
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14290
66.7%
. 7146
33.3%
1 2
 
< 0.1%

bathrooms_na
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.0 KiB
0.0
7125 
1.0
 
21

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters21438
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7125
99.7%
1.0 21
 
0.3%

Length

2023-12-17T22:43:25.675423image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-17T22:43:25.785424image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7125
99.7%
1.0 21
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 14271
66.6%
. 7146
33.3%
1 21
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14292
66.7%
Other Punctuation 7146
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14271
99.9%
1 21
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 7146
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21438
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14271
66.6%
. 7146
33.3%
1 21
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21438
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14271
66.6%
. 7146
33.3%
1 21
 
0.1%

beds_na
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.0 KiB
0.0
7139 
1.0
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters21438
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7139
99.9%
1.0 7
 
0.1%

Length

2023-12-17T22:43:25.902424image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-17T22:43:26.011425image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7139
99.9%
1.0 7
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 14285
66.6%
. 7146
33.3%
1 7
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14292
66.7%
Other Punctuation 7146
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14285
> 99.9%
1 7
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 7146
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21438
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14285
66.6%
. 7146
33.3%
1 7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21438
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14285
66.6%
. 7146
33.3%
1 7
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.0 KiB
0.0
5725 
1.0
1421 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters21438
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5725
80.1%
1.0 1421
 
19.9%

Length

2023-12-17T22:43:26.129423image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-17T22:43:26.239424image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5725
80.1%
1.0 1421
 
19.9%

Most occurring characters

ValueCountFrequency (%)
0 12871
60.0%
. 7146
33.3%
1 1421
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14292
66.7%
Other Punctuation 7146
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12871
90.1%
1 1421
 
9.9%
Other Punctuation
ValueCountFrequency (%)
. 7146
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21438
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12871
60.0%
. 7146
33.3%
1 1421
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21438
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12871
60.0%
. 7146
33.3%
1 1421
 
6.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.0 KiB
0.0
5721 
1.0
1425 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters21438
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5721
80.1%
1.0 1425
 
19.9%

Length

2023-12-17T22:43:26.361425image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-17T22:43:26.473424image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5721
80.1%
1.0 1425
 
19.9%

Most occurring characters

ValueCountFrequency (%)
0 12867
60.0%
. 7146
33.3%
1 1425
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14292
66.7%
Other Punctuation 7146
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12867
90.0%
1 1425
 
10.0%
Other Punctuation
ValueCountFrequency (%)
. 7146
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21438
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12867
60.0%
. 7146
33.3%
1 1425
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21438
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12867
60.0%
. 7146
33.3%
1 1425
 
6.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.0 KiB
0.0
5722 
1.0
1424 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters21438
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5722
80.1%
1.0 1424
 
19.9%

Length

2023-12-17T22:43:26.594424image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-17T22:43:26.709104image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5722
80.1%
1.0 1424
 
19.9%

Most occurring characters

ValueCountFrequency (%)
0 12868
60.0%
. 7146
33.3%
1 1424
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14292
66.7%
Other Punctuation 7146
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12868
90.0%
1 1424
 
10.0%
Other Punctuation
ValueCountFrequency (%)
. 7146
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21438
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12868
60.0%
. 7146
33.3%
1 1424
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21438
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12868
60.0%
. 7146
33.3%
1 1424
 
6.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.0 KiB
0.0
5719 
1.0
1427 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters21438
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5719
80.0%
1.0 1427
 
20.0%

Length

2023-12-17T22:43:26.833105image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-17T22:43:26.942789image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5719
80.0%
1.0 1427
 
20.0%

Most occurring characters

ValueCountFrequency (%)
0 12865
60.0%
. 7146
33.3%
1 1427
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14292
66.7%
Other Punctuation 7146
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12865
90.0%
1 1427
 
10.0%
Other Punctuation
ValueCountFrequency (%)
. 7146
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21438
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12865
60.0%
. 7146
33.3%
1 1427
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21438
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12865
60.0%
. 7146
33.3%
1 1427
 
6.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.0 KiB
0.0
5723 
1.0
1423 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters21438
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5723
80.1%
1.0 1423
 
19.9%

Length

2023-12-17T22:43:27.064793image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-17T22:43:27.178790image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5723
80.1%
1.0 1423
 
19.9%

Most occurring characters

ValueCountFrequency (%)
0 12869
60.0%
. 7146
33.3%
1 1423
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14292
66.7%
Other Punctuation 7146
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12869
90.0%
1 1423
 
10.0%
Other Punctuation
ValueCountFrequency (%)
. 7146
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21438
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12869
60.0%
. 7146
33.3%
1 1423
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21438
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12869
60.0%
. 7146
33.3%
1 1423
 
6.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.0 KiB
0.0
5719 
1.0
1427 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters21438
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5719
80.0%
1.0 1427
 
20.0%

Length

2023-12-17T22:43:27.300791image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-17T22:43:27.418441image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5719
80.0%
1.0 1427
 
20.0%

Most occurring characters

ValueCountFrequency (%)
0 12865
60.0%
. 7146
33.3%
1 1427
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14292
66.7%
Other Punctuation 7146
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12865
90.0%
1 1427
 
10.0%
Other Punctuation
ValueCountFrequency (%)
. 7146
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21438
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12865
60.0%
. 7146
33.3%
1 1427
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21438
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12865
60.0%
. 7146
33.3%
1 1427
 
6.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.0 KiB
0.0
5718 
1.0
1428 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters21438
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5718
80.0%
1.0 1428
 
20.0%

Length

2023-12-17T22:43:27.546207image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-17T22:43:27.658206image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5718
80.0%
1.0 1428
 
20.0%

Most occurring characters

ValueCountFrequency (%)
0 12864
60.0%
. 7146
33.3%
1 1428
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14292
66.7%
Other Punctuation 7146
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12864
90.0%
1 1428
 
10.0%
Other Punctuation
ValueCountFrequency (%)
. 7146
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21438
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12864
60.0%
. 7146
33.3%
1 1428
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21438
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12864
60.0%
. 7146
33.3%
1 1428
 
6.7%

Interactions

2023-12-17T22:43:15.231792image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:41.025963image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:43.195061image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:45.457217image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:47.528536image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:49.677795image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:51.680931image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:53.689357image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:55.827387image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:57.907232image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:00.015332image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:02.292749image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:04.377732image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:06.634644image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:08.715212image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:10.842346image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:13.118799image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:15.344789image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:41.149966image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:43.327888image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:45.578842image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:47.643533image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:49.806793image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:51.793588image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:53.803043image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:55.944388image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:58.030232image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:00.139958image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:02.416952image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:04.498730image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:06.751643image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:08.833890image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:10.964362image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:13.239470image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:15.460787image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:41.304961image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:43.449887image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:45.699841image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:47.760532image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:49.926616image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:51.915596image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:53.919047image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:56.065388image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:58.156232image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:00.273957image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:02.545947image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:04.630739image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:06.878301image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:08.961626image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:11.090348image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:13.364468image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:15.578789image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:41.428962image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:43.568366image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:45.813373image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:47.876537image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:50.041616image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:52.028585image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:54.033050image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:56.183388image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:58.278051image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:00.548101image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:02.667614image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:04.769733image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:06.996302image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:09.080628image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:11.210344image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:13.483470image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:15.689788image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:41.546964image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:43.799022image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:45.928366image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:47.991536image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:50.154293image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:52.161585image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:54.144044image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:56.301385image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:58.396049image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:00.668102image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:02.786610image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:04.888727image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:07.114302image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:09.201282image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:11.331346image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:13.600468image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:15.803789image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:41.668918image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:43.915020image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:46.051369image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:48.104537image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:50.271981image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:52.272588image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:54.260046image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:56.420386image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:58.514049image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:00.784750image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:02.902613image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:05.006727image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:07.233304image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:09.323280image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:11.451251image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:13.722824image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:15.914790image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:41.784916image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:44.043502image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:46.167031image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:48.214536image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:50.383983image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:52.386587image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:54.378046image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:56.535386image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:58.633100image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:00.906748image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:03.022614image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:05.124730image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:07.348302image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:09.440280image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:11.569250image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:13.837826image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:16.022455image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:41.899576image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:44.176510image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:46.288032image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:48.326533image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:50.496993image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:52.500591image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:54.653705image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:56.653387image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:58.751101image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:01.031751image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:03.142612image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:05.246730image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:07.467304image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:09.572307image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:11.841795image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:13.953488image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:16.136454image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:42.026575image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:44.306512image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:46.406029image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:48.443608image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:50.611983image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:52.619586image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:54.771389image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:56.767385image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:58.869748image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:01.153748image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:03.267613image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:05.372728image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:07.605300image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:09.724307image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:11.963462image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:14.075485image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:16.250452image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:42.147576image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:44.429509image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:46.526035image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:48.566138image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:50.728982image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:52.737588image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:54.885386image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:56.894385image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:58.988661image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:01.275751image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:03.391613image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:05.494730image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:07.731301image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:09.848969image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:12.086463image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:14.196487image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:16.377308image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:42.332888image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:44.560510image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:46.652031image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:48.690134image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:50.850983image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:52.857251image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:55.005384image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:57.032386image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:59.119328image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:01.397751image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:03.517319image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:05.626165image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:07.859302image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:09.974966image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:12.216799image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:14.325489image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:16.507307image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:42.457889image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:44.711166image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:46.775029image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:48.807796image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:50.967980image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:52.977251image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:55.124383image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:57.156387image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:59.244331image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:01.525752image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:03.644062image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:05.752163image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:07.979303image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:10.097967image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:12.344798image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:14.468121image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:16.638309image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:42.580742image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:44.835166image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:46.896029image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:48.927794image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:51.085980image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:53.098251image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:55.245388image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:57.282387image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:59.369333image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:01.658751image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:03.770064image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:05.873827image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:08.103303image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:10.220966image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:12.491797image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:14.593121image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:16.937310image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:42.702407image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:44.960167image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:47.029031image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:49.044795image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:51.207649image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:53.222247image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:55.364386image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:57.413068image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:59.506331image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:01.781754image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:03.892731image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:05.994827image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:08.227302image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:10.348692image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:12.631801image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:14.713120image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:17.102311image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:42.822409image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:45.090497image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:47.172879image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:49.317796image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:51.327647image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:53.342251image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:55.483388image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:57.550582image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:59.648334image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:01.909032image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:04.016730image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:06.270824image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:08.350302image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:10.479687image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:12.757799image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:14.836123image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:17.265309image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:42.949411image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:45.217216image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:47.298882image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:49.443796image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:51.450308image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:53.463251image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:55.602385image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:57.673583image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:59.776332image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:02.034036image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:04.139731image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:06.394826image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:08.472301image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:10.604344image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:12.882800image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:14.979122image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:17.442309image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:43.075061image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:45.342217image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:47.421535image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:49.562794image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:51.572928image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:53.583360image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:55.721387image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:57.797249image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:42:59.903328image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:02.158750image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:04.262731image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:06.518642image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:08.594303image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:10.730346image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:13.005800image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T22:43:15.117125image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Missing values

2023-12-17T22:43:17.886307image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-17T22:43:18.476809image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

host_is_superhostcancellation_policyinstant_bookablehost_total_listings_countneighbourhood_cleansedlatitudelongitudeproperty_typeroom_typeaccommodatesbathroomsbedroomsbedsbed_typeminimum_nightsnumber_of_reviewsreview_scores_ratingreview_scores_accuracyreview_scores_cleanlinessreview_scores_checkinreview_scores_communicationreview_scores_locationreview_scores_valuepricebedrooms_nabathrooms_nabeds_nareview_scores_rating_nareview_scores_accuracy_nareview_scores_cleanliness_nareview_scores_checkin_nareview_scores_communication_nareview_scores_location_nareview_scores_value_na
0tmoderatet1.0Western Addition3776931.0-12243386.0ApartmentEntire home/apt3.01.01.02.0Real Bed1.0180.097.010.010.010.010.010.010.0170.00.00.00.00.00.00.00.00.00.00.0
1fstrict_14_with_grace_periodf2.0Bernal Heights3774511.0-12242102.0ApartmentEntire home/apt5.01.02.03.0Real Bed30.0111.098.010.010.010.010.010.09.0235.00.00.00.00.00.00.00.00.00.00.0
2fstrict_14_with_grace_periodf10.0Haight Ashbury3776669.0-1224525.0ApartmentPrivate room2.04.01.01.0Real Bed32.017.085.08.08.09.09.09.08.065.00.00.00.00.00.00.00.00.00.00.0
3fstrict_14_with_grace_periodf10.0Haight Ashbury3776487.0-12245183.0ApartmentPrivate room2.04.01.01.0Real Bed32.08.093.09.09.010.010.09.09.065.00.00.00.00.00.00.00.00.00.00.0
4fstrict_14_with_grace_periodf2.0Western Addition3777525.0-12243637.0HouseEntire home/apt5.0NaN2.02.0Real Bed7.027.097.010.010.010.010.010.09.0785.00.00.00.00.00.00.00.00.00.00.0
5fmoderatef1.0Western Addition3778471.0-12244555.0ApartmentEntire home/apt6.01.02.03.0Real Bed2.031.090.09.08.010.010.09.09.0255.00.00.00.00.00.00.00.00.00.00.0
6tstrict_14_with_grace_periodt2.0Mission3775919.0-12242237.0CondominiumPrivate room3.01.01.02.0Real Bed1.0647.098.010.010.010.010.010.010.0139.00.00.00.00.00.00.00.00.00.00.0
7fstrict_14_with_grace_periodf1.0Potrero Hill3776259.0-12240543.0HousePrivate room2.01.01.01.0Real Bed1.0453.094.010.010.010.010.010.010.0135.00.00.00.00.00.00.00.00.00.00.0
8tmoderatef1.0Mission3775874.0-12241327.0ApartmentEntire home/apt6.01.02.03.0Real Bed3.0320.096.010.010.010.010.010.09.0265.00.00.00.00.00.00.00.00.00.00.0
9fstrict_14_with_grace_periodf44.0Haight Ashbury3777187.0-12243859.0ApartmentEntire home/apt3.01.03.03.0Real Bed30.037.089.09.09.010.09.09.09.0177.00.00.00.00.00.00.00.00.00.00.0
host_is_superhostcancellation_policyinstant_bookablehost_total_listings_countneighbourhood_cleansedlatitudelongitudeproperty_typeroom_typeaccommodatesbathroomsbedroomsbedsbed_typeminimum_nightsnumber_of_reviewsreview_scores_ratingreview_scores_accuracyreview_scores_cleanlinessreview_scores_checkinreview_scores_communicationreview_scores_locationreview_scores_valuepricebedrooms_nabathrooms_nabeds_nareview_scores_rating_nareview_scores_accuracy_nareview_scores_cleanliness_nareview_scores_checkin_nareview_scores_communication_nareview_scores_location_nareview_scores_value_na
7136tmoderatef41.0Russian Hill3780544.0-12242148.0ApartmentEntire home/apt7.02.04.05.0Real Bed30.00.098.010.010.010.010.010.010.0321.00.00.00.01.01.01.01.01.01.01.0
7137fstrict_14_with_grace_periodf67.0Downtown/Civic Center3778733.0-12241152.0HotelPrivate room4.01.00.02.0Real Bed1.00.098.010.010.010.010.010.010.0199.00.00.00.01.01.01.01.01.01.01.0
7138fstrict_14_with_grace_periodf67.0Russian Hill3780434.0-12242081.0HotelPrivate room4.01.01.02.0Real Bed1.00.098.010.010.010.010.010.010.0189.00.00.00.01.01.01.01.01.01.01.0
7139fstrict_14_with_grace_periodf1.0Ocean View3771497.0-12246828.0Guest suiteEntire home/apt5.01.01.01.0Real Bed1.00.098.010.010.010.010.010.010.0125.00.00.00.01.01.01.01.01.01.01.0
7140tflexiblet4.0Visitacion Valley3771456.0-12239917.0ApartmentEntire home/apt6.02.03.03.0Real Bed31.00.098.010.010.010.010.010.010.0148.00.00.00.01.01.01.01.01.01.01.0
7141fflexiblet18.0Noe Valley3774884.0-1224283.0HouseEntire home/apt3.01.01.02.0Real Bed30.00.098.010.010.010.010.010.010.0163.00.00.00.01.01.01.01.01.01.01.0
7142tflexiblet10.0Russian Hill3780645.0-12242109.0Guest suiteEntire home/apt2.01.00.01.0Real Bed1.00.098.010.010.010.010.010.010.0160.00.00.00.01.01.01.01.01.01.01.0
7143tflexiblet10.0Western Addition3778855.0-1224311.0ApartmentEntire home/apt4.01.01.01.0Real Bed2.00.098.010.010.010.010.010.010.0249.00.00.00.01.01.01.01.01.01.01.0
7144fflexiblet87.0Downtown/Civic Center3778645.0-12241458.0ApartmentEntire home/apt3.01.01.02.0Real Bed30.00.098.010.010.010.010.010.010.0105.00.00.00.01.01.01.01.01.01.01.0
7145fflexiblef87.0Downtown/Civic Center3778645.0-12241458.0ApartmentEntire home/apt5.01.02.02.0Real Bed30.00.098.010.010.010.010.010.010.0125.00.00.00.01.01.01.01.01.01.01.0